A database has become the core component of most computer application software nowadays. Typically application software makes use of a single or multiple databases as repositories of data (content) required by the application to function properly. The application's operational efficiency and availability is greatly dependent on the performance and availability of these database(s), which can be measured by two metrics: (1) request response time; and (2) transaction throughput.
There are several techniques for improving application efficiency based on these two metrics: (1) Vertical scale up of computer hardware supporting the application—this is achieved by adding to or replacing existing hardware with faster processors such as central processing units (CPUs), random access memory (RAM), disk adapters/controllers, and network; and (2) Horizontal scale out (clustering) of computer hardware supporting the application—this approach refers to connecting additional computing hardware to the existing configuration by interconnecting them with a fast network. Although both approaches can address the need of reducing request response time and increase transaction throughput, the scale out approach can offer higher efficiency at lower costs, thus driving most new implementations into clustering architecture.
The clustering of applications can be achieved readily by running the application software on multiple, interconnected application servers that facilitate the execution of the application software and provide hardware redundancy for high availability, with the application software actively processing requests concurrently. However current database clustering technologies cannot provide the level of availability and redundancy in a similar active-active configuration. Consequently database servers are primarily configured as active-standby, meaning that one of the computer systems in the cluster does not process application request until a failover occurs. Active-standby configuration wastes system resources, extends the windows of unavailability and increases the chance of data loss.
To cluster multiple database servers in an active-active configuration, one technical challenge is to resolve update conflict. An update conflict refers to two or more database servers updating the same record in the databases that they manage. Since data in these databases must be consistent among them in order to scale out for performance and achieve high availability, the conflict must be resolved. Currently there are two different schemes of conflict resolution: (1) time based resolution; and (2) location based resolution. However, neither conflict resolution schemes can be enforced without some heuristic decision to be made by human intervention. It is not possible to determine these heuristic decision rules unless there is a thorough understanding of the application software business rules and their implications. Consequently, most clustered database configurations adopt the active-standby model, and fail to achieve high performance and availability at the same time. There is a need for providing a database management system that uses an active-active configuration and substantially reduces the possibility of update conflicts that may occur when two or more databases attempt to update a record at the same time.
The systems and methods disclosed herein provide a system for globally managing transaction requests to one or more database servers and to obviate or mitigate at least some of the above presented disadvantages.